RT Journal
A1 Atak, Zeynep Kalender
A1 Taskiran, Ibrahim Ihsan
A1 Demeulemeester, Jonas
A1 Flerin, Christopher
A1 Mauduit, David
A1 Minnoye, Liesbeth
A1 Hulselmans, Gert
A1 Christiaens, Valerie
A1 Ghanem, Ghanem Elias
A1 Wouters, Jasper
A1 Aerts, Stein
T1 Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning
JF Genome Research 
JO Genome Research 
YR 2021 
FD April 08 
DO 10.1101/gr.260851.120 
SP gr.260851.120 
UL http://genome.cshlp.org/content/early/2021/04/08/gr.260851.120.abstract 
AB Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation as well as additional ETS motif gains can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.